Neural subsets 3WT integrated analysis R Notebook

suppressPackageStartupMessages({
  library(Seurat)
  library(org.Dr.eg.db)
  library(BSgenome.Drerio.UCSC.danRer11)
  library(Signac)
  library(knitr)
  library(kableExtra)
  library(dplyr)
  library(ggplot2)
  library(ggsci)
  library(limma)
  library(JASPAR2020)
  library(patchwork)
  library(TFBSTools)
  library(motifmatchr)
  library(AnnotationHub)
  library(harmony)
})

options(future.globals.maxSize = 4000 * 1024^2)

Read data

int.3WT <- readRDS(file = "RDSfiles/int.neural.3WT.RDS")

p1 <- DimPlot(int.3WT, reduction = "wnn.umap", group.by = "wsnn_res.0.8", label = T, repel = T) + scale_color_igv() + NoLegend()

p2 <- DimPlot(int.3WT, reduction = "wnn.umap", group.by = "Clusters", label = T, repel = T) + scale_color_igv() + NoLegend()

p1 + p2

Increase cluster resolution

# remove old individual cluster resolutions
int.3WT$wsnn_res.1 <- NULL
int.3WT$wsnn_res.1.5 <- NULL
int.3WT$wsnn_res.2 <- NULL
int.3WT$wsnn_res.2.5 <- NULL
int.3WT$wsnn_res.3 <- NULL
int.3WT$wsnn_res.3.5 <- NULL
int.3WT$wsnn_res.6 <- NULL
int.3WT$wsnn_res.8 <- NULL

int.3WT <- FindClusters(int.3WT, graph.name = "wsnn", resolution = 2, algorithm = 3, verbose = FALSE)
int.3WT <- FindClusters(int.3WT, graph.name = "wsnn", resolution = 3, algorithm = 3, verbose = FALSE)
int.3WT <- FindClusters(int.3WT, graph.name = "wsnn", resolution = 4, algorithm = 3, verbose = FALSE)
int.3WT <- FindClusters(int.3WT, graph.name = "wsnn", resolution = 5, algorithm = 3, verbose = FALSE)
int.3WT <- FindClusters(int.3WT, graph.name = "wsnn", resolution = 6, algorithm = 3, verbose = FALSE)

p1 <- DimPlot(int.3WT, reduction = "wnn.umap", group.by = "wsnn_res.2", label = T, repel = T) + scale_color_igv() + NoLegend()

p2 <- DimPlot(int.3WT, reduction = "wnn.umap", group.by = "wsnn_res.3", label = T, repel = T) + scale_color_igv() + NoLegend()

p3 <- DimPlot(int.3WT, reduction = "wnn.umap", group.by = "wsnn_res.4", label = T, repel = T) + scale_color_igv() + NoLegend()

p4 <- DimPlot(int.3WT, reduction = "wnn.umap", group.by = "wsnn_res.5", label = T, repel = T) + scale_color_igv() + NoLegend()

p5 <- DimPlot(int.3WT, reduction = "wnn.umap", group.by = "wsnn_res.6", label = T, repel = T) + scale_color_igv() + NoLegend()

p6 <- DimPlot(int.3WT, reduction = "wnn.umap", group.by = "Clusters", label = T, repel = T) + scale_color_igv() + NoLegend()

(p1 + p2 + p3) / (p4 + p5 + p6)

Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider increasing max.overlaps

saveRDS(int.3WT, file = "RDSfiles/int.neural.3WT.RDS")

Resolution Note

Based on what I’m seeing resolution 5 might be what we want since it separates r1, r2, r1&r2, r4 and DorsNT into separate clusters. Resolution 6 only further resolves cluster 5 in res 5 (MHB) into clusters 18 and 28 in res 6.

Res 5 clustering split by Sample

p1 <- DimPlot(int.3WT, reduction = "wnn.umap", group.by = "wsnn_res.5", split.by = "orig.ident", label = T, repel = T) + scale_color_igv()
p1

Original Cluster names split by Sample

DimPlot(int.3WT, reduction = "wnn.umap", group.by = "Clusters", split.by = "orig.ident", label = T, repel = T, ncol = 3) + scale_color_igv() + NoLegend()

Tables of Cell Counts

as.data.frame(table(int.3WT$Clusters, int.3WT$wsnn_res.5)) %>%
  tidyr::pivot_wider(names_from = "Var2", values_from = "Freq") %>%
  rename("Cluster" = "Var1") %>%
  mutate(Cluster = gsub(" ","",Cluster)) %>%
  mutate(across(everything(), gsub, pattern = "^0$", replacement = ".")) %>%
  kbl(caption = "Cell Count Orig Clusters by Res 5 clusters") %>%
  kable_classic(full_width = F, "striped")

Cell Count Orig Clusters by Res 5 clusters

Cluster	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41
CHB.1	46	19	.	14	.	.	.	3	45	.	.	.	1	.	.	.	.	.	.	4	.	.	.	.	4	.	.	.	.	1	.	1	.	.	6	.	1	.	.	.	.	1
CHB.2	71	.	.	41	.	.	.	1	3	.	.	.	.	.	.	.	.	.	.	2	.	.	.	.	.	.	.	1	.	.	.	.	.	.	14	.	.	1	.	.	.	4
CHB.3	.	2	.	60	.	.	.	2	29	.	.	.	.	2	.	.	.	.	.	.	.	.	.	.	1	.	.	.	1	1	.	.	.	1	14	.	.	.	.	.	.	1
CHB.4	8	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	10	.	.	.	.	.	.	.	.	.	.	.	1
Ciliated	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	14	.	.	.	44	.	1	.	.	.	.	.	1	1	.	2	.	.
DorsNT&NC	.	.	4	.	.	.	.	.	.	.	.	.	29	.	.	.	.	.	.	.	23	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.
FB	.	.	.	.	2	.	.	.	.	.	1	10	.	.	1	.	.	.	35	.	.	.	.	16	.	.	1	.	2	3	4	.	5	.	.	.	.	.	1	.	.	.
FB&eye.2	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	8	2	.	.	.	.	.	.	.	1	15	.	.	.	.	.	9	.	.	.
FB&eye.1	.	.	.	.	.	1	.	.	.	.	.	37	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	2	.	.	.	.	.	.	.	.	.	.	.	.
FB&eye.3	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	17	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	8	.	.	.
FB&eye.4	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	7	1	.	.	.	.	.	.	.	.	9	.	.	.	.	.	4	.	.	.
FB&eye.5	.	.	.	.	.	16	.	.	.	.	.	.	.	.	2	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.
FB&eye.6	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	17	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.
FB&eye.7	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	5	1	.	.	.	.	.	.	.	.	4	.	.	.	.	.	2	.	.	.
FB&eye.8	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.
FB.1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	35	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	1
FB.2	.	.	.	.	.	.	.	1	.	.	.	26	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.
FB.3	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	23	.	.	.	.	2	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.
FB.4	.	.	.	.	1	.	.	.	.	1	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	21	.	.	.	.	.	.	.	.	.	.	.
HB.1	.	.	19	.	.	.	.	.	.	.	.	.	8	6	.	7	2	.	.	.	3	2	.	.	.	1	.	.	.	5	.	.	.	.	.	.	.	2	.	.	1	1
HB.2	1	.	.	2	.	.	.	.	.	.	.	.	.	7	.	.	.	1	.	.	2	1	.	.	.	.	.	4	.	1	.	.	.	.	1	.	.	21	.	.	.	.
HB.3	2	.	.	.	.	.	.	1	.	.	.	.	.	1	.	.	.	.	.	.	5	.	.	.	.	.	.	.	.	2	.	.	1	.	.	.	.	2	.	.	.	.
MB	.	.	.	.	11	2	.	.	.	.	1	.	.	.	29	.	.	.	.	.	.	1	.	.	.	.	2	.	.	2	1	.	.	.	.	.	.	.	.	.	.	.
MB.1	.	.	.	.	36	1	.	.	.	1	.	1	.	.	34	.	.	.	2	.	.	.	.	.	.	.	4	.	.	2	12	.	.	.	.	.	.	.	1	.	.	.
MB.2	.	.	.	.	31	21	.	.	.	1	.	.	.	.	5	.	.	.	.	.	1	.	.	.	.	.	6	.	.	2	.	.	1	.	.	.	.	.	.	.	.	1
MB.3	.	.	.	.	30	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
MHB.1	.	.	.	.	7	2	58	1	.	20	.	.	1	.	.	2	.	.	.	.	.	.	.	.	.	24	8	.	.	2	1	.	.	.	.	.	.	.	.	.	3	.
MHB.2	.	.	.	.	.	44	17	.	.	16	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	7	7	.	.	1	.	.	.	.	.	.	.	.	.	.	11	1
MHB.3	.	.	.	.	.	22	24	.	.	14	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	2	24	.	.	2	.	.	.	1	.	.	.	.	.	.	1	1
MHB.4	.	.	.	.	1	3	.	.	.	24	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	2	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.
MHB.5	.	.	.	.	.	.	4	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	16	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
NC.1	.	.	.	.	.	.	.	1	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	1	.	2	20	.	.	20	.	.
NC.2	6	.	.	.	.	.	.	2	.	.	.	.	.	.	.	.	.	.	.	4	.	.	.	.	1	.	.	.	.	.	.	.	.	.	1	.	2	1	.	.	.	.
Neuron	.	.	.	.	.	.	.	62	.	.	.	.	.	.	.	.	.	.	1	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	34	1	.	.	.	1	.
r1	.	.	.	.	.	.	2	.	.	.	.	.	4	1	.	59	1	.	.	.	.	.	.	.	.	2	.	.	.	.	.	.	.	10	.	.	.	1	.	.	.	.
r1&r2	.	.	.	.	.	.	.	1	.	.	.	.	22	.	.	1	4	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
r2	.	.	.	.	.	.	.	.	.	.	.	.	3	5	.	.	59	.	.	.	1	.	.	.	.	.	.	.	1	.	.	.	.	23	.	.	.	.	.	.	.	.
r3	.	.	98	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	2
r4	.	.	.	.	.	.	.	1	1	.	.	.	.	48	.	.	1	.	.	.	23	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	.	1
r5	.	.	1	.	.	.	.	.	.	.	.	.	1	.	.	.	.	20	.	.	.	20	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	1
r5.1	.	.	2	.	.	.	.	.	.	.	.	.	.	.	.	.	.	42	.	.	.	1	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.
r5.2	.	.	2	.	.	.	.	.	.	.	.	.	.	.	.	.	.	3	.	.	.	35	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
r6	1	.	.	.	.	.	.	.	.	.	.	.	2	.	.	.	.	.	.	.	.	.	.	.	.	.	.	45	.	.	.	37	.	1	.	.	.	.	.	.	.	.
SC.1	.	68	.	.	.	.	.	5	1	.	42	.	1	.	.	.	.	.	.	1	.	.	.	.	21	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.
SC.2	.	40	.	3	.	.	.	2	1	.	11	.	.	.	.	.	.	.	.	21	1	.	.	.	16	.	.	.	.	.	.	.	1	.	1	.	2	.	.	.	.	1
SC.3	.	4	.	.	.	.	.	.	.	.	21	.	.	.	.	.	.	.	.	30	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	3	.	.	5	.	.

as.data.frame(table(int.3WT$orig.ident, int.3WT$wsnn_res.5)) %>%
  tidyr::pivot_wider(names_from = "Var2", values_from = "Freq") %>%
  rename("Sample" = "Var1") %>%
  kbl(caption = "Cell Count Sample by Res 5 clusters") %>%
  kable_classic(full_width = F, "striped")

Cell Count Sample by Res 5 clusters

Sample	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41
HB10hpf	27	43	19	10	20	39	18	7	5	22	55	37	9	15	31	7	2	1	32	10	11	4	56	4	21	10	15	4	3	19	6	1	36	1	36	2	22	27	25	23	16	5
HB13hpf	46	38	45	44	61	39	50	26	31	37	12	27	26	24	32	27	35	20	24	22	23	20	1	38	14	20	22	24	24	8	21	16	2	10	0	18	7	1	3	2	0	10
HB16hpf	62	52	62	66	38	34	37	51	44	19	10	10	38	33	8	35	30	45	6	30	26	36	1	16	22	24	15	23	22	17	13	22	0	27	1	16	1	1	0	2	1	2

as.data.frame(table(int.3WT$Clusters, int.3WT$orig.ident)) %>%
  tidyr::pivot_wider(names_from = "Var2", values_from = "Freq") %>%
  rename("Cluster" = "Var1") %>%
  mutate(Cluster = gsub(" ","",Cluster)) %>%
  mutate(across(everything(), gsub, pattern = "^0$", replacement = ".")) %>%
  kbl(caption = "Cell Count Orig Clusters by Sample") %>%
  kable_classic(full_width = F, "striped")

Cell Count Orig Clusters by Sample

Cluster	HB10hpf	HB13hpf	HB16hpf
CHB.1	38	51	57
CHB.2	36	46	56
CHB.3	24	38	52
CHB.4	.	.	20
Ciliated	.	28	36
DorsNT&NC	.	.	57
FB	49	.	32
FB&eye.2	35	.	.
FB&eye.1	41	.	.
FB&eye.3	26	.	.
FB&eye.4	22	.	.
FB&eye.5	19	.	.
FB&eye.6	18	.	.
FB&eye.7	12	.	.
FB&eye.8	2	.	.
FB.1	.	37	.
FB.2	.	29	.
FB.3	.	26	.
FB.4	.	25	.
HB.1	57	.	.
HB.2	41	.	.
HB.3	14	.	.
MB	49	.	.
MB.1	.	37	57
MB.2	.	36	33
MB.3	.	30	.
MHB.1	39	45	45
MHB.2	37	35	33
MHB.3	31	34	27
MHB.4	.	31	.
MHB.5	.	21	.
NC.1	46	.	.
NC.2	17	.	.
Neuron	.	37	63
r1	.	39	41
r1&r2	.	29	.
r2	.	37	55
r3	.	47	54
r4	.	38	38
r5	.	44	.
r5.1	.	.	46
r5.2	.	.	40
r6	.	38	48
SC.1	49	44	47
SC.2	38	31	31
SC.3	16	17	30

as.data.frame(table(int.3WT$Clusters[int.3WT$orig.ident == "HB10hpf"], int.3WT$wsnn_res.5[int.3WT$orig.ident == "HB10hpf"])) %>%
  tidyr::pivot_wider(names_from = "Var2", values_from = "Freq") %>%
  rename("Cluster" = "Var1") %>%
  mutate(Cluster = gsub(" ","",Cluster)) %>%
  mutate(across(everything(), gsub, pattern = "^0$", replacement = ".")) %>%
  kbl(caption = "HB10hpf cell count") %>%
  kable_classic(full_width = F, "striped")

HB10hpf cell count

Cluster	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41
CHB.1	.	18	.	1	.	.	.	2	3	.	.	.	.	.	.	.	.	.	.	3	.	.	.	.	4	.	.	.	.	1	.	.	.	.	6	.	.	.	.	.	.	.
CHB.2	18	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	14	.	.	1	.	.	.	1
CHB.3	.	.	.	7	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	14	.	.	.	.	.	.	1
FB	.	.	.	.	2	.	.	.	.	.	1	.	.	.	.	.	.	.	31	.	.	.	.	.	.	.	1	.	2	2	4	.	5	.	.	.	.	.	1	.	.	.
FB&eye.2	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	8	2	.	.	.	.	.	.	.	1	15	.	.	.	.	.	9	.	.	.
FB&eye.1	.	.	.	.	.	1	.	.	.	.	.	37	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	2	.	.	.	.	.	.	.	.	.	.	.	.
FB&eye.3	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	17	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	8	.	.	.
FB&eye.4	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	7	1	.	.	.	.	.	.	.	.	9	.	.	.	.	.	4	.	.	.
FB&eye.5	.	.	.	.	.	16	.	.	.	.	.	.	.	.	2	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.
FB&eye.6	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	17	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.
FB&eye.7	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	5	1	.	.	.	.	.	.	.	.	4	.	.	.	.	.	2	.	.	.
FB&eye.8	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.
HB.1	.	.	19	.	.	.	.	.	.	.	.	.	8	6	.	7	2	.	.	.	3	2	.	.	.	1	.	.	.	5	.	.	.	.	.	.	.	2	.	.	1	1
HB.2	1	.	.	2	.	.	.	.	.	.	.	.	.	7	.	.	.	1	.	.	2	1	.	.	.	.	.	4	.	1	.	.	.	.	1	.	.	21	.	.	.	.
HB.3	2	.	.	.	.	.	.	1	.	.	.	.	.	1	.	.	.	.	.	.	5	.	.	.	.	.	.	.	.	2	.	.	1	.	.	.	.	2	.	.	.	.
MB	.	.	.	.	11	2	.	.	.	.	1	.	.	.	29	.	.	.	.	.	.	1	.	.	.	.	2	.	.	2	1	.	.	.	.	.	.	.	.	.	.	.
MHB.1	.	.	.	.	7	2	.	.	.	17	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	8	.	.	.	1	.	.	.	.	.	.	.	.	.	3	.
MHB.2	.	.	.	.	.	.	16	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	7	.	.	.	1	.	.	.	.	.	.	.	.	.	.	11	1
MHB.3	.	.	.	.	.	18	2	.	.	5	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	4	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.
NC.1	.	.	.	.	.	.	.	1	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	1	.	2	20	.	.	20	.	.
NC.2	6	.	.	.	.	.	.	2	.	.	.	.	.	.	.	.	.	.	.	4	.	.	.	.	1	.	.	.	.	.	.	.	.	.	1	.	2	1	.	.	.	.
SC.1	.	6	.	.	.	.	.	.	.	.	42	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
SC.2	.	16	.	.	.	.	.	.	1	.	2	.	.	.	.	.	.	.	.	.	1	.	.	.	16	.	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	1
SC.3	.	3	.	.	.	.	.	.	.	.	8	.	.	.	.	.	.	.	.	2	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	3	.	.

as.data.frame(table(int.3WT$Clusters[int.3WT$orig.ident == "HB13hpf"], int.3WT$wsnn_res.5[int.3WT$orig.ident == "HB13hpf"])) %>%
  tidyr::pivot_wider(names_from = "Var2", values_from = "Freq") %>%
  rename("Cluster" = "Var1") %>%
  mutate(Cluster = gsub(" ","",Cluster)) %>%
  mutate(across(everything(), gsub, pattern = "^0$", replacement = ".")) %>%
  kbl(caption = "HB13hpf cell count") %>%
  kable_classic(full_width = F, "striped")

HB13hpf cell count

Cluster	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41
CHB.1	46	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	1	.	.	.	.	1
CHB.2	.	.	.	39	.	.	.	.	3	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	3
CHB.3	.	2	.	2	.	.	.	2	27	.	.	.	.	2	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	1	.	.	.	1	.	.	.	.	.	.	.	.
Ciliated	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	4	.	.	.	23	.	.	.	.	.	.	.	.	.	.	.	.	.
FB.1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	35	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	1
FB.2	.	.	.	.	.	.	.	1	.	.	.	26	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.
FB.3	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	23	.	.	.	.	2	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.
FB.4	.	.	.	.	1	.	.	.	.	1	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	21	.	.	.	.	.	.	.	.	.	.	.
MB.1	.	.	.	.	1	1	.	.	.	.	.	1	.	.	30	.	.	.	1	.	.	.	.	.	.	.	.	.	.	2	.	.	.	.	.	.	.	.	1	.	.	.
MB.2	.	.	.	.	28	.	.	.	.	1	.	.	.	.	2	.	.	.	.	.	1	.	.	.	.	.	1	.	.	1	.	.	1	.	.	.	.	.	.	.	.	1
MB.3	.	.	.	.	30	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
MHB.1	.	.	.	.	.	.	43	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.
MHB.2	.	.	.	.	.	32	1	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
MHB.3	.	.	.	.	.	3	.	.	.	9	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	20	.	.	.	.	.	.	1	.	.	.	.	.	.	.	1
MHB.4	.	.	.	.	1	3	.	.	.	24	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	2	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.
MHB.5	.	.	.	.	.	.	4	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	16	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
Neuron	.	.	.	.	.	.	.	18	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	18	1	.	.	.	.	.
r1	.	.	.	.	.	.	2	.	.	.	.	.	1	1	.	26	1	.	.	.	.	.	.	.	.	2	.	.	.	.	.	.	.	5	.	.	.	1	.	.	.	.
r1&r2	.	.	.	.	.	.	.	1	.	.	.	.	22	.	.	1	4	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
r2	.	.	.	.	.	.	.	.	.	.	.	.	.	4	.	.	30	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	3	.	.	.	.	.	.	.	.
r3	.	.	44	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	2
r4	.	.	.	.	.	.	.	1	.	.	.	.	.	16	.	.	.	.	.	.	21	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
r5	.	.	1	.	.	.	.	.	.	.	.	.	1	.	.	.	.	20	.	.	.	20	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	1
r6	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	23	.	.	.	15	.	.	.	.	.	.	.	.	.	.
SC.1	.	31	.	.	.	.	.	3	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	9	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
SC.2	.	5	.	3	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	21	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	2	.	.	.	.	.
SC.3	.	.	.	.	.	.	.	.	.	.	12	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	3	.	.	2	.	.

as.data.frame(table(int.3WT$Clusters[int.3WT$orig.ident == "HB16hpf"], int.3WT$wsnn_res.5[int.3WT$orig.ident == "HB16hpf"])) %>%
  tidyr::pivot_wider(names_from = "Var2", values_from = "Freq") %>%
  rename("Cluster" = "Var1") %>%
  mutate(Cluster = gsub(" ","",Cluster)) %>%
  mutate(across(everything(), gsub, pattern = "^0$", replacement = ".")) %>%
  kbl(caption = "HB16hpf cell count") %>%
  kable_classic(full_width = F, "striped")

HB16hpf cell count

Cluster	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41
CHB.1	.	1	.	13	.	.	.	1	42	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
CHB.2	53	.	.	2	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
CHB.3	.	.	.	51	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
CHB.4	8	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	10	.	.	.	.	.	.	.	.	.	.	.	1
Ciliated	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	10	.	.	.	21	.	1	.	.	.	.	.	1	1	.	2	.	.
DorsNT&NC	.	.	4	.	.	.	.	.	.	.	.	.	29	.	.	.	.	.	.	.	23	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.
FB	.	.	.	.	.	.	.	.	.	.	.	10	.	.	1	.	.	.	4	.	.	.	.	16	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.
MB.1	.	.	.	.	35	.	.	.	.	1	.	.	.	.	4	.	.	.	1	.	.	.	.	.	.	.	4	.	.	.	12	.	.	.	.	.	.	.	.	.	.	.
MB.2	.	.	.	.	3	21	.	.	.	.	.	.	.	.	3	.	.	.	.	.	.	.	.	.	.	.	5	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.
MHB.1	.	.	.	.	.	.	15	1	.	3	.	.	.	.	.	2	.	.	.	.	.	.	.	.	.	23	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.
MHB.2	.	.	.	.	.	12	.	.	.	15	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	6	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
MHB.3	.	.	.	.	.	1	22	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	2	.	.	.	.	.	.	.	.	.	.	.	.
Neuron	.	.	.	.	.	.	.	44	.	.	.	.	.	.	.	.	.	.	1	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	16	.	.	.	.	1	.
r1	.	.	.	.	.	.	.	.	.	.	.	.	3	.	.	33	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	5	.	.	.	.	.	.	.	.
r2	.	.	.	.	.	.	.	.	.	.	.	.	3	1	.	.	29	.	.	.	1	.	.	.	.	.	.	.	1	.	.	.	.	20	.	.	.	.	.	.	.	.
r3	.	.	54	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
r4	.	.	.	.	.	.	.	.	1	.	.	.	.	32	.	.	1	.	.	.	2	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	.	1
r5.1	.	.	2	.	.	.	.	.	.	.	.	.	.	.	.	.	.	42	.	.	.	1	.	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.	.	.
r5.2	.	.	2	.	.	.	.	.	.	.	.	.	.	.	.	.	.	3	.	.	.	35	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
r6	1	.	.	.	.	.	.	.	.	.	.	.	2	.	.	.	.	.	.	.	.	.	.	.	.	.	.	22	.	.	.	22	.	1	.	.	.	.	.	.	.	.
SC.1	.	31	.	.	.	.	.	2	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	12	.	.	.	.	1	.	.	.	.	.	.	.	.	.	.	.	.
SC.2	.	19	.	.	.	.	.	2	.	.	9	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	.
SC.3	.	1	.	.	.	.	.	.	.	.	1	.	.	.	.	.	.	.	.	28	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.

Remove FB and FB&eye clusters

Rational: the amount of FB or FB&eye was dissection specific and not possible to precisely control.

After examining Cell Count Original Clusters by Res 5 clusters will remove clusters: 11, 18, 22, 23, 32 and 38

Will keep 29 – this has lots of cells from a variety of clusters and may be interesting Will keep 5 – this is in with MB clusters and may have been mislabeled previously.
Will also keep 30 since this more closely associated with MB clusters than FB clusters.

Idents(int.3WT) <- "wsnn_res.5"
int.3WT.subset <- subset(int.3WT, idents = c("11","18","22","23","32","38"), invert = TRUE)
DimPlot(int.3WT.subset, reduction = "wnn.umap", label = T, repel = T) + scale_color_igv()

Res 5 clustering split by Sample

p1 <- DimPlot(int.3WT.subset, reduction = "wnn.umap", group.by = "wsnn_res.5", split.by = "orig.ident", label = T, repel = T) + scale_color_igv()
p1

Original Cluster names split by Sample

DimPlot(int.3WT.subset, reduction = "wnn.umap", group.by = "Clusters", split.by = "orig.ident", label = T, repel = T, ncol = 3) + scale_color_igv() + NoLegend()

Idents(int.3WT.subset) <- "Clusters"
HB1cells <- WhichCells(int.3WT.subset, idents = "HB.1")
HB2cells <- WhichCells(int.3WT.subset, idents = "HB.2")
HB3cells <- WhichCells(int.3WT.subset, idents = "HB.3")
p1 <- DimPlot(int.3WT.subset, group.by = "wsnn_res.5", cells.highlight = HB1cells, reduction = "wnn.umap") + NoLegend() + ggtitle("HB.1 cells")

p2 <- DimPlot(int.3WT.subset, group.by = "wsnn_res.5", cells.highlight = HB2cells, reduction = "wnn.umap") + NoLegend() + ggtitle("HB.2 cells")

p3 <- DimPlot(int.3WT.subset, group.by = "wsnn_res.5", cells.highlight = HB3cells, reduction = "wnn.umap") + NoLegend() + ggtitle("HB.3 cells")

p4 <- DimPlot(int.3WT.subset, group.by = "wsnn_res.5", reduction = "wnn.umap", label = TRUE, repel = T) + NoLegend() + scale_color_igv()

(p1 + p2) / (p3 + p4)

Since there are still a few cells which were originally called FB or FB&eye in other clusters, these labels are still in the metadata table and are still cluttering up the UMAP. So will remove all labels with “FB” in them from UMAP of cluster labels.

int.3WT.subset$fixedClusters <- int.3WT.subset$Clusters
int.3WT.subset$fixedClusters[grep("FB", int.3WT.subset$fixedClusters)] <- " "

p1 <- DimPlot(int.3WT.subset, group.by = "fixedClusters", cells.highlight = HB1cells, reduction = "wnn.umap") + NoLegend() + ggtitle("HB.1 cells")

p2 <- DimPlot(int.3WT.subset, group.by = "fixedClusters", cells.highlight = HB2cells, reduction = "wnn.umap") + NoLegend() + ggtitle("HB.2 cells")

p3 <- DimPlot(int.3WT.subset, group.by = "fixedClusters", cells.highlight = HB3cells, reduction = "wnn.umap") + NoLegend() + ggtitle("HB.3 cells")

p4 <- DimPlot(int.3WT.subset, group.by = "fixedClusters", reduction = "wnn.umap", label = TRUE, repel = T) + NoLegend() + scale_color_igv()

(p1 + p2) / (p3 + p4)

Idents(int.3WT.subset) <- "wsnn_res.5"
All.markers <- FindAllMarkers(int.3WT.subset, only.pos = TRUE)

write.table(All.markers, file = "../results/int3WT.subset.markers.res.5.txt", sep = "\t", quote = F, col.names = NA)

cellCount <- as.data.frame(table(int.3WT.subset$Clusters, int.3WT.subset$wsnn_res.5, int.3WT.subset$orig.ident)) %>%
  dplyr::filter(!Freq == 0)
write.table(cellCount, file = "../results/int3WT.cellCount.res.5.txt", quote = F, sep = "\t", row.names = FALSE)

After determining cluster cell types, adding as “intClusters”

DimPlot(int.3WT.subset, group.by = "wsnn_res.5", reduction = "wnn.umap", label = TRUE, repel = T) + NoLegend() + scale_color_igv()

Idents(int.3WT.subset) <- "wsnn_res.5"
int.3WT.subset <- RenameIdents(int.3WT.subset,
                               "0" = "CaudHB.1",
                               "1" = "SC.1",
                               "2" = "r3.1",
                               "3" = "CaudHB.2",
                               "4" = "MB.1",
                               "5" = "MHB.1",
                               "6" = "MHB.2",
                               "7" = "Neuron.1",
                               "8" = "CaudHB.3",
                               "9" = "MHB.3",
                               "10" = "SC.2",
                               "12" = "r1&r2.1",
                               "13" = "r4.1",
                               "14" = "MB.2",
                               "15" = "r1",
                               "16" = "r2",
                               "17" = "r5.1",
                               "19" = "SC.3",
                               "20" = "r4.2",
                               "21" = "r5.2",
                               "24" = "SC.4",
                               "25" = "MHB.4",
                               "26" = "MHB.5",
                               "27" = "r6.1",
                               "28" = "Ciliated",
                               "29" = "Neurog",
                               "30" = "MB.3",
                               "31" = "r6.2",
                               "33" = "r1&r2.2",
                               "34" = "CaudHB.4",
                               "35" = "Neuron.2",
                               "36" = "NC.1",
                               "37" = "HB",
                               "39" = "NC.2",
                               "40" = "MHB.6",
                               "41" = "Mitochondrial")
int.3WT.subset$intClusters <- Idents(int.3WT.subset)

DimPlot(int.3WT.subset, reduction = "wnn.umap", label = TRUE, repel = T) + NoLegend() + scale_color_igv() + ggtitle("Integrated Neural Clusters")

saveRDS(int.3WT.subset, file = "RDSfiles/int.neural.3WT.subset.RDS")

sessionInfo()

R version 4.1.0 (2021-05-18)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS 12.3

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats4    stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] ensembldb_2.18.0                    AnnotationFilter_1.18.0            
 [3] GenomicFeatures_1.46.1              AnnotationHub_3.2.0                
 [5] BiocFileCache_2.2.0                 dbplyr_2.1.1                       
 [7] harmony_0.1.0                       Rcpp_1.0.7                         
 [9] motifmatchr_1.16.0                  TFBSTools_1.32.0                   
[11] patchwork_1.1.1                     JASPAR2020_0.99.10                 
[13] limma_3.50.3                        ggsci_2.9                          
[15] ggplot2_3.3.5                       dplyr_1.0.7                        
[17] kableExtra_1.3.4                    knitr_1.36                         
[19] Signac_1.2.1                        BSgenome.Drerio.UCSC.danRer11_1.4.2
[21] BSgenome_1.62.0                     rtracklayer_1.54.0                 
[23] Biostrings_2.62.0                   XVector_0.34.0                     
[25] GenomicRanges_1.46.0                GenomeInfoDb_1.30.0                
[27] org.Dr.eg.db_3.14.0                 AnnotationDbi_1.56.1               
[29] IRanges_2.28.0                      S4Vectors_0.32.4                   
[31] Biobase_2.54.0                      BiocGenerics_0.40.0                
[33] SeuratObject_4.0.4                  Seurat_4.0.1                       

loaded via a namespace (and not attached):
  [1] rappdirs_0.3.3                SnowballC_0.7.0               scattermore_0.7              
  [4] R.methodsS3_1.8.1             ragg_1.2.2                    tidyr_1.1.4                  
  [7] nabor_0.5.0                   bit64_4.0.5                   irlba_2.3.3                  
 [10] DelayedArray_0.20.0           R.utils_2.11.0                data.table_1.14.2            
 [13] rpart_4.1-15                  KEGGREST_1.34.0               RCurl_1.98-1.5               
 [16] generics_0.1.1                cowplot_1.1.1                 RSQLite_2.2.8                
 [19] RANN_2.6.1                    chromVAR_1.16.0               future_1.26.1                
 [22] bit_4.0.4                     tzdb_0.2.0                    spatstat.data_2.1-0          
 [25] webshot_0.5.2                 xml2_1.3.2                    httpuv_1.6.3                 
 [28] SummarizedExperiment_1.24.0   assertthat_0.2.1              DirichletMultinomial_1.36.0  
 [31] xfun_0.27                     hms_1.1.1                     jquerylib_0.1.4              
 [34] evaluate_0.14                 promises_1.2.0.1              progress_1.2.2               
 [37] fansi_0.5.0                   restfulr_0.0.13               caTools_1.18.2               
 [40] igraph_1.2.8                  DBI_1.1.1                     htmlwidgets_1.5.4            
 [43] sparsesvd_0.2                 spatstat.geom_2.3-0           purrr_0.3.4                  
 [46] ellipsis_0.3.2                RSpectra_0.16-0               backports_1.3.0              
 [49] annotate_1.72.0               biomaRt_2.50.0                deldir_1.0-6                 
 [52] MatrixGenerics_1.6.0          vctrs_0.4.1                   ROCR_1.0-11                  
 [55] abind_1.4-5                   cachem_1.0.6                  withr_2.4.2                  
 [58] ggforce_0.3.3                 checkmate_2.0.0               sctransform_0.3.3            
 [61] GenomicAlignments_1.30.0      prettyunits_1.1.1             goftest_1.2-3                
 [64] svglite_2.0.0                 cluster_2.1.2                 ape_5.5                      
 [67] lazyeval_0.2.2                seqLogo_1.60.0                crayon_1.4.2                 
 [70] pkgconfig_2.0.3               slam_0.1-48                   labeling_0.4.2               
 [73] tweenr_1.0.2                  ProtGenerics_1.26.0           nlme_3.1-153                 
 [76] nnet_7.3-16                   rlang_1.0.3                   globals_0.15.1               
 [79] lifecycle_1.0.1               miniUI_0.1.1.1                filelock_1.0.2               
 [82] dichromat_2.0-0               polyclip_1.10-0               matrixStats_0.61.0           
 [85] lmtest_0.9-38                 Matrix_1.3-4                  ggseqlogo_0.1                
 [88] zoo_1.8-9                     base64enc_0.1-3               ggridges_0.5.3               
 [91] png_0.1-7                     viridisLite_0.4.0             rjson_0.2.20                 
 [94] bitops_1.0-7                  R.oo_1.24.0                   KernSmooth_2.23-20           
 [97] blob_1.2.2                    stringr_1.4.0                 parallelly_1.32.0            
[100] jpeg_0.1-9                    readr_2.0.2                   CNEr_1.30.0                  
[103] scales_1.1.1                  memoise_2.0.0                 magrittr_2.0.1               
[106] plyr_1.8.6                    ica_1.0-2                     zlibbioc_1.40.0              
[109] compiler_4.1.0                BiocIO_1.4.0                  RColorBrewer_1.1-2           
[112] fitdistrplus_1.1-6            Rsamtools_2.10.0              cli_3.3.0                    
[115] listenv_0.8.0                 pbapply_1.5-0                 htmlTable_2.3.0              
[118] Formula_1.2-4                 MASS_7.3-54                   mgcv_1.8-38                  
[121] tidyselect_1.1.1              stringi_1.7.5                 textshaping_0.3.6            
[124] highr_0.9                     yaml_2.2.1                    latticeExtra_0.6-29          
[127] ggrepel_0.9.1                 grid_4.1.0                    VariantAnnotation_1.40.0     
[130] sass_0.4.0                    fastmatch_1.1-3               tools_4.1.0                  
[133] future.apply_1.8.1            parallel_4.1.0                rstudioapi_0.13              
[136] TFMPvalue_0.0.8               foreign_0.8-81                lsa_0.73.2                   
[139] gridExtra_2.3                 farver_2.1.0                  Rtsne_0.15                   
[142] digest_0.6.28                 BiocManager_1.30.16           shiny_1.7.1                  
[145] pracma_2.3.3                  qlcMatrix_0.9.7               BiocVersion_3.14.0           
[148] later_1.3.0                   RcppAnnoy_0.0.19              httr_1.4.2                   
[151] rsconnect_0.8.24              biovizBase_1.42.0             colorspace_2.0-2             
[154] rvest_1.0.2                   XML_3.99-0.8                  tensor_1.5                   
[157] reticulate_1.22               splines_4.1.0                 uwot_0.1.10                  
[160] RcppRoll_0.3.0                spatstat.utils_2.2-0          renv_0.15.5                  
[163] plotly_4.10.0                 systemfonts_1.0.4             xtable_1.8-4                 
[166] jsonlite_1.7.2                poweRlaw_0.70.6               R6_2.5.1                     
[169] Hmisc_4.6-0                   pillar_1.6.4                  htmltools_0.5.2              
[172] mime_0.12                     glue_1.6.2                    fastmap_1.1.0                
[175] DT_0.19                       BiocParallel_1.28.0           interactiveDisplayBase_1.32.0
[178] codetools_0.2-18              utf8_1.2.2                    lattice_0.20-45              
[181] bslib_0.3.1                   spatstat.sparse_2.0-0         tibble_3.1.6                 
[184] curl_4.3.2                    leiden_0.3.9                  gtools_3.9.2                 
[187] GO.db_3.14.0                  survival_3.2-13               rmarkdown_2.11               
[190] docopt_0.7.1                  munsell_0.5.0                 GenomeInfoDbData_1.2.7       
[193] reshape2_1.4.4                gtable_0.3.0                  spatstat.core_2.3-0